Video Management Systems are used for video data acquisition and search processes using single or multiple servers. They are often loosely coupled with one or more separate systems for performing operations on the acquired video data such as analyzing the video content, etc. Servers can record different types of data in storage media, and the storage media can be directly attached to the servers or accessed over IP network. This demands a significant amount of network bandwidth to receive data from the sensors (e.g, Cameras) and to concurrently transfer or upload the data in the storage media. Due to high demand in bandwidth to perform such tasks, especially for video data, often separate high speed network are dedicated to transfer data to storage media. Dedicated high speed network is costly and often require costly storage devices as well. Often this is overkill for low or moderately priced installations.
It is also known that to back up against server failures, one or more dedicated fail-over (sometimes called mirror) servers are often deployed in prior art. Dedicated fail-over servers remain unused during normal operations and hence resulting in wastage of such costly resources. Also, a central server process either installed in the failover server or in a central server is required to initiate the back-up service, in case a server stops operating. This strategy does not avoid a single point of failure.
Moreover, when the servers and clients reside over different ends in an internet and the connectivity suffers from low or widely varying bandwidth, transmission of multi-channel data from one point to another becomes a challenge. Data aggregation techniques are often applied in such cases which are computationally intensive or suffer from inter-channel interference, particularly for video, audio or other types of multimedia data.
As regards analytic servers presently in use it is well known that there are many video analytics system in the prior art. Video content analysis is often done per frame basis which is mostly pre defined which make such systems lacking in desired efficiency of analytics but are also unnecessarily cost extensive with unwanted loss of valuable computing resources.
Added to the above, in case of presently available techniques of video analysis, cases of unacceptable number of false alarms are reported when the content analysis systems are deployed in a noisy environment for generating alerts in real time. This is because the traditional methods are not automatically adaptive to demography specific environmental conditions, varying illumination levels, varying behavioural and movement patterns of the moving objects in a scene, changes of appearance of colour in varying lighting conditions, changes of appearance of colours in global or regional illumination intensity and type of illumination, and similar other factors.
It has therefore been a challenge to identify the appearance of a non-moving foreign object (static object) in a scene in presence of other moving objects, where the moving objects occasionally occlude the static object. Detection accuracy suffers in various degrees under different demographic conditions.
Extraction of particular types of objects (e.g. face of a person, but not limited to) in images based on fiduciary points is a known technique. However, computational requirement is often too high for traditional classifier used for this purpose in the prior art, e.g., Haar classifier.
Also, in a distributed system where multiple sites with independent administrative controls are present, unification of those systems through a central monitoring station may be required at any later point of time. This necessitates hardware and OS independence in addition to the backward compatibility of the underlying computational infrastructure components, and the software architecture should accommodate such amalgamation as well.
It would be thus clearly apparent from the above state of the art that there is need for advancement in the art of sensory input/data such as video acquisition cum recording and/or analytics of such sensory inputs/data such as video feed adapted to facilitate fail-safe integration and/or optimized utilization of various sensory inputs for various utility applications including event/alert generation, recording and related aspects.